35 45 600/5
ConvNeXt SOTA performance on farm vs game fowl. Green border indicates
correct classification whereas red border indicates incorrect classification.
Train/Test images obtained from Kaggle, Reddit, and Google.
1. Motivation
2. Challenges
Using ConvNeXt and transfer learning, our implementation achieves
breed-specific labeling and, when not available, differentiation between
game fowl and domestic fowl.
3. Initial Modeling of Chicken Body Shape
4. Experiments
▪ We experimented with four different models:
▪ Baseline CNN, horrible classification when
other animals were introduced (10-15%).
▪ ResNet50, decent classification, but not good
enough to be breed specific
▪ ConvNeXt, good breed specific classification
and excellent species classification(90% top5)
▪ ViT, mediocre species classification(~35%)
5. Results
Above: Model complexity. All
images use adam and cross
entropy loss
Right: Comparison to various
models in terms of top 1 test ,
training accuracy and time to
detect .
There are times that our model didn't classify images even remotely correct.
For example, the middle image was classified as a lapdog.
6. Future Work
▪ Refinement of ConvNeXt labeling techniques
▪ Applying ConvNeXt to video as compared to images
▪ More broad data collection strategies and sources
▪ Try DeiT and other modern transformers.
Visual depiction of game
versus farm fowl
▪ Given two broad
categories of chicken
game and farm
▪ Estimate social media
websites for chicken data
▪ Also, jointly estimate
type of chicken, game or
farm based for diseased
protection and isolation
▪ Need to identify contours and feather data properly
▪ Deeper layers perform better with more expressive
details documented
▪ Model needs to be as discriminative as possible, so
we train against similar looking animals (e.g.
squirrels)
▪ Handle different species, color, and orientation of
chickens including texture of feathers.
▪ Solve limited data issues with stitching,
translation, and warping
▪ Model needs to be of the "chickens versus the
world" type, where chickens are classified against
other animals / observable items in the world.
▪ Out-of-the-box computer vision models (ResNet10,
ResNet50, etc.) tend to have a hard time classifying
different breeds of the same species
▪ This problem becomes increasingly more
pronounced with data scraped from various social
media sources (Twitter, Craigslist, etc.)
▪ We propose a way to use state-of-the-art (SOTA)
computer vision models to accurately label different
breeds of the same species (in our case, Chickens)
▪ We utilize ConvNeXt and transfer learning to achieve
breed specific classification where possible
Acknowledgements: This project is in collaboration with the CE Poultry Lab at the University of California – Davis.